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breast-cancer-classification

This project aims to perform binary classification to detect presence of cancerous cells in histopathological scans. The images are taken from the histopathological scans of lymph node sections from Kaggle Histopathological cancer detection challenge and provide tumor visualizations of tumor tissues.

cardiac-segmentation-unet

Deep learning has found it's use in the medical imaging community for diagnostic and post processing methods. One such application is the medical image segmentation using Unet.

control-system

controltoolbox

corlab

csit_gui_tool

This is an open source GUI based tool CSIT (Control Systems Interactive Toolbox), that can be used to analyze and learn Control Systems. The tool was developed under the FOSSEE project at Indian Institute of Technology, Bombay.

deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

gui-codes

CSIT : GUI

intensity-based-mri-registration

Image registration is one of the prior steps for building computational model and Computer added diagnosis (CAD) which is the processes of transferring images into a common coordinate system, so that corresponding pixels represents homologous biological points. In this lab, we have familiarized with the concepts and framework of image registration based on two different transformation techniques namely “rigid transformation” and “affine transformation” for brain MRI. Comparisons also have been accomplished for single-resolution and multi-resolution registration for the same images in both rigid transformation and affine transformation. Different quantitative and qualitative metric performance are also been observed for all the experiments.

leader-follower

machine-learning

This repository consists of Machine Learning assignments for the course CIS 519

mit-deep-learning

Tutorials, assignments, and competitions for MIT Deep Learning related courses.

mnist-reconstruction-analysis

This works is an analysis of the feature space and comparison of the discriminative capability of features obtained from Autoencoders .In Deep learning literature these problems statements are called representation learning and through this work MNIST feature space and it's intricacies are visualized .